MegaTrans – human transporter machine learning models

MegaTrans — 人类运输机机器学习模型

• 批准号: 10546264
• 负责人: Nathan J Cherrington
• 金额: $ 86.48万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546264
• 关键词: 3-Dimensional; Agrochemicals; Algorithms; Angiotensin-Converting Enzyme Inhibitors; Antiviral Agents; Arizona; Bayesian Method; Bayesian learning; Behavior; Biological Assay; Blood-Testis Barrier; COVID-19 treatment; CRISPR/Cas technology; Chemistry; Client; Clinical; Code; Collaborations; Collection; Computer Models; Computer software; Consult; Data; Data Set; Databases; Decision Trees; Descriptor; Docking; Drug Design; Drug Industry; Drug Interactions; Drug Modelings; Evaluation; Family; Fee-for-Service Plans; Fingerprint; Foundations; Graph; Hela Cells; Hepatocyte; Human; In Vitro; Industry; International; Intuition; Investments; Learning; Libraries; Licensing; Ligands; Literature; Machine Learning; Mediating; Methods; Modeling; Molecular; Natural Products; Nucleoside Transporter; Online Systems; Organ; Output; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Process; Property; PubChem; Public Domains; Pythons; Receiver Operating Characteristics; Reporting; Resources; Risk; Seminal fluid; Site; Software Tools; Structure; Structure-Activity Relationship; System; Testing; Toxic Environmental Substances; Toxic effect; Training; Trees; Universities; Uridine; Validation; Vendor; Virus; Visualization; Work; Xenobiotics; base; clinically relevant; computerized tools; consumer product; data curation; deep learning; design; drug candidate; drug discovery; drug disposition; high throughput screening; improved; in vitro testing; in vivo; inhibitor; inhibitor therapy; interest; long short term memory; machine learning algorithm; machine learning method; machine learning model; member; model building; molecular shape; molnupiravir; neural network; novel therapeutics; pharmacophore; predictive modeling; prospective; prototype; random forest; remdesivir; side effect; software development; tool; tool development; uptake; web app

Summary Being able to predict interactions with important human transporters would be of value to new drug design to avoid compounds that interact with them and cause undesirable side effects. Conversely, some drug transporters can be used for targeting molecules to specific organs and this may have considerable utility. Understanding the interactions of novel drugs, natural products and environmental toxicants and their interactions with an array of such transporters is, therefore, important for several industries, as well as from a regulatory perspective (e.g. FDA, EPA and EMA). Being able to predict such interactions in a fast and reliable manner effectively requires using computational approaches and learning from in vitro data, the latter a resource that is rapidly growing. Over the past 20 years, we have been at the forefront of applying different machine learning approaches to modeling drug transporters and, in many cases, developing datasets for transporters for which there was scant available data. We now propose doing this for several transporters that may be important for drug discovery. In Phase I we focused on OATP1B1 (SLCO1B1), which is an uptake transporter largely restricted to the sinusoidal aspect of hepatocytes where it mediates transport of a variety of structurally unrelated compounds, including members of several clinically important drug families (incl. statins, sartans and angiotensin converting enzyme (ACE) inhibitors). We tested 476 drugs against one substrate in vitro. We then curated these data and built machine learning models using multiple machine learning methods as well as model evaluation metrics. This enabled us to develop models for integration in a web-based software tool called MegaTrans® that enables the user to input their own compound structures and generate predictions for interactions with transporter/s of interest, as well as visualize the similarity to the training set of each model using several different visualization methods. In addition, during Phase I we also performed preliminary data curation, model building and validation for two equilibrative nucleoside transporters (ENTs), ENT1 and ENT2, that are present at the blood testes barrier (BTB), where they can facilitate drug disposition (e.g. for antivirals, thereby potentially eliminating a sanctuary site for viruses detectable in semen). We generated Bayesian and pharmacophore models and used these to predict numerous compounds that were then tested in vitro against ENTs. We used these ENT models to predict (i) the antivirals used in treating COVID-19, remdesivir and molnupiravir, inhibit ENT activity, and that (ii) remdesivir is an ENT substrate, as well as validating these predictions. In Phase II we plan on building on the foundation of Phase I and propose greatly expanding the ENT1 and ENT2 models through in vitro testing (at the University of Arizona) of >2000 approved drugs, natural products, and environmental toxicants as inhibitors of ENT transport. We will use these data to build and validate machine learning models using several algorithms, at Collaborations Pharmaceuticals, Inc. We will also test these models using external validation with additional molecules from vendor libraries and drug collections that are not in the model. In this process we will also build out the capabilities of MegaTransÒ to use 3D pharmacophore descriptors to incorporate molecular shape features and allow 3D searches. The return on investment of such a commercial tool would be that it could assist in the design and selection of more favorable compounds by avoiding transporters of interest (or, conversely, allow the targeting of specific transporters to increase uptake into organs). It could also identify compounds that are already approved that might present a drug-interaction risk. Predicting such behavior seen in vivo is ideal and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In summary, we propose generating large training sets for ENT1 and ENT2 transporters that we will use to generate an array of validated machine learning models of interest to drug discovery (with specific interest for those generating antivirals). MegaTransÒ will be a commercial product available for licensing by pharmaceutical, consumer product, agrochemical and regulatory groups, as well as fee-for-service consulting provided by Collaborations Pharmaceuticals, Inc.

摘要 能够预测与重要的人类转运蛋白的相互作用将对新药设计具有价值 避免与它们相互作用并引起不良副作用的化合物。相反，一些毒品运输商 可用于将分子靶向特定器官，这可能具有相当大的实用价值。了解 新型药物、天然产物和环境毒物的相互作用及其与一系列 因此，这种运输商对几个行业以及从监管的角度来说都很重要(例如： FDA、EPA和EMA)。要以快速可靠的方式有效地预测此类交互，需要 使用计算方法并从体外数据中学习，后者是一种正在迅速增长的资源。 在过去的20年里，我们一直处于将不同的机器学习方法应用于 对药物转运体进行建模，并在许多情况下为缺乏转运体的转运体开发数据集 可用的数据。我们现在建议对几种可能对药物发现很重要的转运蛋白进行这项研究。在……里面 第一阶段我们专注于OATP1B1(SLCO1B1)，它是一种摄取转运体，主要局限于正弦 肝细胞的一个方面，它调节各种结构上无关的化合物的运输，包括 几个临床重要药物家族的成员(包括他汀类药物、沙坦和血管紧张素转换酶 (ACE)抑制剂)。我们在体外针对一种底物测试了476种药物。然后，我们整理了这些数据，并建立了 使用多种机器学习方法的机器学习模型以及模型评估指标。这 使我们能够开发集成到基于Web的软件工具MegaTrans®中的模型，该工具支持 用户输入自己的复合结构并生成与Transporter/S互动的预测 兴趣，以及使用几种不同的可视化来可视化每个模型的训练集的相似性 方法：研究方法。此外，在第一阶段，我们还进行了初步的数据整理、模型构建和验证 对于存在于血睾丸屏障的两个平衡的核苷转运体(Ents)，ENT1和Ent2 (Btb)，在那里它们可以促进药物处置(例如，用于抗病毒药物，从而可能消除避难所) 精液中可检测到病毒的站点)。我们生成了贝叶斯和药效团模型，并使用这些模型 预测大量化合物，然后在体外测试这些化合物对ENTs的作用。我们使用这些耳鼻喉科模型来预测 (I)治疗新冠肺炎时使用的抗病毒药物瑞美西韦和莫诺匹韦可抑制耳鼻喉科的活性，以及(Ii) Redesivir是一种ENT底物，并验证了这些预测。在第二阶段，我们计划在 建立第一阶段，并建议通过体外测试大幅扩展ENT1和ENT2模型(在 亚利桑那大学)2000年批准的药物、天然产品和环境毒物作为 登陆车。我们将使用这些数据使用几种算法来构建和验证机器学习模型， 在Collaborations PharmPharmticals，Inc.，我们还将使用外部验证和其他 来自供应商图书馆和药品收藏的分子不在模型中。在这个过程中，我们还将建立 发挥MegaTrans的能力，使用3D药效团描述符来整合分子形状 功能，并允许3D搜索。这种商业工具的投资回报将是它可以帮助 在通过避免感兴趣的转运体(或者，相反， 允许靶向特定的转运体以增加器官的摄取)。它还可以识别出 已经被批准，可能会带来药物相互作用的风险。在活体中预测这种行为是理想的 并将导致化合物的优先顺序，以在体外测试潜在的药物-药物相互作用。总而言之， 我们建议为ENT1和ENT2传输器生成大型训练集，我们将使用它们来生成数组 对药物发现感兴趣的经过验证的机器学习模型(对那些生成的模型特别感兴趣 抗病毒药物)。MegaTrans将是一种商业产品，可供制药、消费者 产品、农用化学品和监管小组，以及协作提供的收费服务咨询 制药公司